4 ms·
Tangent: I guess the silver lining in proofreading mistakes is that the content wasn't generated by ChatGPT.
by curiousgal 4y ago
Tangent: I guess the silver lining in proofreading mistakes is that the content wasn't generated by ChatGPT.
- expertentipp 4y ago> setting aside 1 billion Swiss francs ($1.1 million). Billion, million, googol, whatever. For CNN and Swiss bankers it doesn't make difference. Executives assuring that everything is all calm and orderly means how many breaths it has left?
- moffkalast 4y agoI mean it's a billion CHF, Michael, what could it cost? 10 dollars?
- expertentipp 4y agoI'm afraid we have bad news for dollar-speaking investors, sir.
- TheCleric 4y agoThere's always money in the banking stand.
- withzombies 4y agoIs this one of those billion = long million mistakes in some languages? Not everyone agrees that a billion is 1000 million and it can get really confusing sometimes. > Other countries use the word billion (or words cognate to it) to denote either the long scale or short scale billion. (For details, see Long and short scales § Current usage.) > Milliard, another term for one thousand million, is extremely rare in English, but words similar to it are very common in other European languages. https://en.wikipedia.org/wiki/Billion#:~:text=Other%20countries%20use%20the%20word%20billion%20%28or%20words,it%20are%20very%20common%20in%20other%20European%20languages https://en.wikipedia.org/wiki/Billion#:~:text=Other%20countr....
- expertentipp 4y agoBillion and Milliard are commonly mistaken between SI and "imperial" countries. Million (10^6) is rather unequivocal.
- deleted 4y ago[deleted]
- antibasilisk 4y agoChatGPT can generate proofreading mistakes
- CWuestefeld 4y agoIf it's built from content on the web, how can it avoid doing so?
- pixelpoet 4y agoDictionaries are pretty effective
- tialaramex 4y agoProbabilities matter. So the machine ends up with a model of human language, in which errors are present but uncommon, same trick a child is doing. For other reasons I was looking recently at the Wug test. If you run that test on very young kids in Japan, two year olds pass no problems, in Japanese there's no plurals so, "1 Wug" => "2 Wug". But English native kids at this age struggle, "1 Wug" => ??? they're aware there's a rule for how this works, but they aren't yet confident what the rule is. A year or two later, "1 Wug" => "2 Wugs" they have learned the rule, make plurals by adding a -S sound to the word. I expect ChatGPT can pass the wug test. In fact, unlike a random two year old it will certainly have read about the actual Wug test, so definitely don't ask it about that word in particular, make up a new word. Now, human kids are learning a spoken language, the model is learning a written language, but they're both linear so it's not that different.